Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals.

ABSTRACT: Nowadays, Electrocardiogram (ECG) signals can be measured using wearable devices, such as smart watches. Most wearable devices provide only a few details; however, they have the advantage of recording data in real time. In this study, 12-lead ECG signals were generated from lead I and their feasibility was tested to obtain more details. The 12-lead ECG signals were generated using a U-net-based generative adversarial network (GAN) that was trained on ECG data obtained from the Asan Medical Center. Subsequently, unseen PTB-XL PhysioNet data were used to produce real 12-lead ECG signals for classification. The generated and real 12-lead ECG signals were then compared using a ResNet classification model; and the normal, atrial fibrillation (A-fib), left bundle branch block (LBBB), right bundl

SUBMITTER: Yoon GW 

PROVIDER: S-EPMC10803292 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature